Examining Demographics, Prior Academic Performance, and United States Medical Licensing Examination Scores
Bibliographic record
Abstract
PURPOSE: To examine whether demographic differences exist in United States Medical Licensing Examination (USMLE) scores and the extent to which any differences are explained by students' prior academic achievement. METHOD: The authors completed hierarchical linear modeling of data for U.S. and Canadian allopathic and osteopathic medical graduates testing on USMLE Step 1 during or after 2010, and completing USMLE Step 3 by 2015. Main outcome measures were computer-based USMLE examinations: Step 1, Step 2 Clinical Knowledge, and Step 3. Test-taker characteristics included sex, self-identified race, U.S. citizenship status, English as a second language, and age at first Step 1 attempt. Covariates included composite Medical College Admission Test (MCAT) scores, undergraduate grade point average (GPA), and previous USMLE scores. RESULTS: A total of 45,154 examinees from 172 medical schools met the inclusion criteria. The sample was 67% white and 48% female; 3.7% non-U.S. citizens; and 7.4% with English as a second language. Hierarchical linear models examined demographic variables with and without covariates including MCAT scores and GPA. All Step examinations showed significant differences by gender after adding covariates, varying by Step. Racial differences were observed for each Step, attenuated by the addition of covariates. CONCLUSIONS: Demographic differences in USMLE performance were tempered by previous examination performance and undergraduate performance. Additional research is required to identify factors that contribute to demographic differences, can aid educators' identification of students who would benefit from assistance preparing for USMLE, and can assist residency program directors in assessing performance measures while meeting diversity goals.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".